In general, it is much cheaper and faster to obtain rough segmentations, either manual or computer-generated, on large training datasets. However, rather than treating such approximate segmentation labels as ground truth, as done in [1]}, one can use more intelligent methods. A comprehensive recent review of deep learning with noisy labels with a focus on medical image analysis can be found in [2]}. Several recent studies have reported successful applications of such methods for medical image segmentation [3]}, [4]}, [5]}.
